An Application of Machine Learning Techniques for Loan Default Payment Prediction - Inchamnam, Wilawan; Kajornrit, Jesada; Jirapanthong, Waraporn (JOURNAL OF INFORMATION SCIENCE AND TECHNOLOGY, 2024)
In the banking business, predicting customer default payments has become a crucial operation to prevent and mitigate risks caused by non-performing loans. Presently, machine learning techniques are used alongside traditional methods for this task. This paper explores several ways to apply machine learning techniques in predicting default payments. The prediction development framework includes data encoding, data sampling, and model development. At each step, various techniques are tested and compared to find optimal solutions for business requirements. Our findings conclude that ensemble models are a good choice over a single model to increase the precision of the default payment class. The Over-sampling method is a suitable choice to increase recall of the default payment class, whereas the Under-sampling method is not recommended. Furthermore, if the size of the input vector is a concern, the Weight of Evidence encoding method can be used instead of One-hot encoding without a loss in performance.